Fast 3-d surface multiple prediction

ABSTRACT

A method and apparatus for predicting a plurality of surface multiples for a plurality of traces in a record of seismic data. In one embodiment, the method includes providing a plurality of target traces at a nominal offset and a nominal azimuth; selecting a plurality of pairs of input traces, wherein the midpoints of the input traces in each pair are separated by half the nominal offset and the azimuth of a line connecting the midpoints of the input traces in each pair is equal to the nominal azimuth; convolving the selected pairs of input traces to generate a plurality of convolutions; and applying a three dimensional operator to the convolutions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims benefit of U.S. provisional patent applicationSer. No. 60/560,223, filed Apr. 7, 2004, which is herein incorporated byreference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

Embodiments of the present invention generally relate to marine seismicsurveying and, more particularly, to a method for attenuating the effectof surface multiples in seismic signals.

2. Description of Related Art

Seismic surveying is a method for determining the structure ofsubterranean formations in the earth. Seismic surveying typicallyutilizes seismic energy sources which generate seismic waves and seismicreceivers which detect seismic waves. The seismic waves propagate intothe formations in the earth, where a portion of the waves reflects frominterfaces between subterranean formations. The amplitude and polarityof the reflected waves are determined by the differences in acousticimpedance between the rock layers comprising the subterraneanformations. The acoustic impedance of a rock layer is the product of theacoustic propagation velocity within the layer and the density of thelayer. The seismic receivers detect the reflected seismic waves andconvert the reflected waves into representative electrical signals. Thesignals are typically transmitted by electrical, optical, radio or othermeans to devices which record the signals. Through analysis of therecorded signals (or traces), the shape, position and composition of thesubterranean formations can be determined.

Marine seismic surveying is a method for determining the structure ofsubterranean formations underlying bodies of water. Marine seismicsurveying typically utilizes seismic energy sources and seismicreceivers located in the water which are either towed behind a vessel orpositioned on the water bottom from a vessel. The energy source istypically an explosive device or compressed air system which generatesseismic energy, which then propagates as seismic waves through the bodyof water and into the earth formations below the bottom of the water. Asthe seismic waves strike interfaces between subterranean formations, aportion of the seismic waves reflects back through the earth and waterto the seismic receivers, to be detected, transmitted, and recorded. Theseismic receivers typically used in marine seismic surveying arepressure sensors, such as hydrophones. Additionally, though, motionsensors, such as accelerometers may be used. Both the sources andreceivers may be strategically repositioned to cover the survey area.

Seismic waves, however, reflect from interfaces other than just thosebetween subterranean formations, as would be desired. Seismic waves alsoreflect from the water bottom and the water surface, and the resultingreflected waves themselves continue to reflect. Waves which reflectmultiple times are called “multiples”. Waves which reflect multipletimes in the water layer between the water surface above and the waterbottom below are called “water-bottom multiples”. Water-bottom multipleshave long been recognized as a problem in marine seismic processing andinterpretation, so multiple attenuation methods based on the waveequation have been developed to handle water-bottom multiples. However,a larger set of multiples containing water-bottom multiples as a subsetcan be defined. The larger set includes multiples with upwardreflections from interfaces between subterranean formations in additionto upward reflections from the water bottom. The multiples in the largerset have in common their downward reflections at the water surface andthus are called “surface multiples”. FIG. 1, discussed below, providesexamples of different types of reflections.

FIG. 1 shows a diagrammatic view of marine seismic surveying. Theprocedure is designated generally as 100. Subterranean formations to beexplored, such as 102 and 104, lie below a body of water 106. Seismicenergy sources 108 and seismic receivers 110 are positioned in the bodyof water 106, typically by one or more seismic vessels (not shown). Aseismic source 108, such as an air gun, creates seismic waves in thebody of water 106 and a portion of the seismic waves travels downwardthrough the water toward the subterranean formations 102 and 104 beneaththe body of water 106. When the seismic waves reach a seismic reflector,a portion of the seismic waves reflects upward and a portion of theseismic waves continues downward. The seismic reflector can be the waterbottom 112 or one of the interfaces between two subterranean formations,such as interface 114 between formations 102 and 104. When the reflectedwaves traveling upward reach the water/air interface at the watersurface 116, a majority portion of the waves reflects downward again.Continuing in this fashion, seismic waves can reflect multiple timesbetween upward reflectors, such as the water bottom 112 or formationinterfaces below, and the downward reflector at the water surface 116above, as described more fully below. Each time the reflected wavespropagate past the position of a seismic receiver 110, the receiver 110senses the reflected waves and generates representative signals.

Primary reflections are those seismic waves which have reflected onlyonce, from the water bottom 112 or an interface between subterraneanformations, before being detected by a seismic receiver 110. An exampleof a primary reflection is shown in FIG. 1 by raypaths 120 and 122.Primary reflections contain the desired information about thesubterranean formations which is the goal of marine seismic surveying.Surface multiples are those waves which have reflected multiple timesbetween the water surface 116 and any upward reflectors, such as thewater bottom 112 or formation interfaces, before being sensed by areceiver 110. An example of a surface multiple which is specifically awater bottom multiple is shown by raypaths 130, 132, 134 and 136. Thepoint on the water surface 116 at which the wave is reflected downwardis generally referred to as the downward reflection point 133. Thesurface multiple starting at raypath 130 is a multiple of order one,since the multiple contains one reflection from the water surface 116.Two examples of general surface multiples with upward reflections fromboth the water bottom 112 and formation interfaces are shown by raypaths140, 142, 144, 146, 148 and 150 and by raypaths 160, 162, 164, 166, 168and 170. Both of these latter two examples of surface multiples aremultiples of order two, since the multiples contain two reflections fromthe water surface 116. In general, a surface multiple is of order i ifthe multiple contains i reflections from the water surface 116. Surfacemultiples are extraneous noise which obscures the desired primaryreflection signal.

Surface multiple attenuation is a prestack inversion of a recordedwavefield which removes all orders of all surface multiples presentwithin the marine seismic signal. Unlike some wave-equation-basedmultiple-attenuation algorithms, surface multiple attenuation does notrequire any modeling of or assumptions regarding the positions, shapesand reflection coefficients of the multiple-causing reflectors. Instead,surface multiple attenuation relies on the internal physical consistencybetween primary and multiple events that must exist in any properlyrecorded marine data set. The information needed for the surfacemultiple attenuation process is already contained within the seismicdata.

Various prior art methods have been tried for removal of surfacemultiples from recorded traces. It has been noted, for example, that thetravel time for a water bottom multiple is a function of the “offset”,the distance between the source and receiver, and the number of timesthe multiple reflects from the surface. For example, if the multiplereflects from the surface once before being received by the microphoneand the offset is zero, the multiple's travel time is exactly twice thatof the principal waves. This fact has been used in various schemes toremove multiples.

Other methods involve complex ray tracing schemes which generate asynthetic multiple wave and subtract it from the actual wave to obtain asupposedly multiple free record. However, these methods are very awkwardin that they require significant knowledge of the subsea structure aswell as the ocean bottom configuration before the synthetic wave can begenerated. Similar synthetic multiples can be generated using moreaccurate methods not directly involving ray tracing, e.g., fieldpropagation techniques, but again these require detailed knowledge of atleast the ocean bottom, as well as the shape of the subsea interfaces,and so are not as practical as would be desired.

Therefore, a need exists in the art for an improved method for removingthe record of multiple surface reflection events from seismic recordsfor seismic data processing purposes.

SUMMARY OF THE INVENTION

Embodiments of the present invention are generally directed to a methodfor predicting a plurality of surface multiples for a plurality oftraces in a record of seismic data. In one embodiment, the methodincludes providing a plurality of target traces at a nominal offset anda nominal azimuth; selecting a plurality of pairs of input traces,wherein the midpoints of the input traces in each pair are separated byhalf the nominal offset and the azimuth of a line connecting themidpoints of the input traces in each pair is equal to the nominalazimuth; convolving the selected pairs of input traces to generate aplurality of convolutions; and applying a three dimensional operator tothe convolutions.

In another embodiment, the method includes dividing a plurality oftarget traces into one or more groups according to offsets; dividingeach group into one or more subgroups according to azimuths; selecting afirst subgroup having a first nominal offset and a first nominalazimuth; selecting a plurality of pairs of input traces, wherein themidpoints of the input traces in each pair are separated by half thefirst nominal offset and the azimuth of a line connecting the midpointsof the input traces in each pair is equal to the first nominal azimuth;convolving the selected pairs of input traces to generate a plurality ofconvolutions; and applying a three dimensional operator to theconvolutions.

In yet another embodiment, the method includes providing a plurality oftarget traces at a nominal offset; predicting a plurality of twodimensional surface multiples for a plurality of input subsurface lines;and applying a two dimensional operator to the predicted two dimensionalsurface multiples.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the presentinvention can be understood in detail, a more particular description ofthe invention, briefly summarized above, may be had by reference toembodiments, some of which are illustrated in the appended drawings. Itis to be noted, however, that the appended drawings illustrate onlytypical embodiments of this invention and are therefore not to beconsidered limiting of its scope, for the invention may admit to otherequally effective embodiments.

FIG. 1 illustrates a diagrammatic view of marine seismic surveying.

FIG. 2 a flow diagram of a method for performing a three dimensionalsurface multiple prediction in accordance with one or more embodimentsof the invention.

FIG. 3 illustrates a flow diagram of a method for performing a threedimensional surface multiple prediction in accordance with one or moreembodiments of the invention.

FIG. 4 illustrates a plan view of an acquisition geometry in accordancewith one or more embodiments of the invention.

FIG. 5 illustrates a computer network into which various embodiments ofthe invention may be implemented.

DETAILED DESCRIPTION

FIG. 2 illustrates a flow diagram of a method 200 for performing a threedimensional surface multiple prediction in accordance with one or moreembodiments of the invention. At step 210, a set of target traces isdivided into one or more groups according to offsets. Each groupcontains target traces with offsets within a specified range, where anoffset is defined as the horizontal distance between a source and areceiver. The set of target traces defines the locations at which themultiples are to be predicted. The offset ranges span from the shortestoffset in the set of target traces, which typically corresponds to thereceiver located closest to the back of the vessel, e.g., about 100meters, to the longest offset in the set of target traces, whichtypically corresponds to the receiver furthest from the back of thevessel, e.g., about 6000 meters. Each offset range has a nominal offsetvalue, which is typically the central offset value in that range. Forexample, target traces with offsets between 100 meters and 150 metersmay be organized into one range with a nominal offset value of 125meters, and target traces with offsets between 150 meters and 200 metersmay be organized into another range with a nominal offset value of 175meters.

At step 220, a group, such as a first group, of the target traces withoffsets within a first range of offsets is selected. At step 230, theselected group of the target traces is divided into one or moresubgroups of target traces according to azimuths. An azimuth is definedas the angle between the line that connects the source and the receiverand some fixed direction, which is typically the in-line direction. Eachazimuth range has a nominal value, which is typically the central valueof that range. In this manner, all the target traces within a subgroupare at the same or similar azimuths (or have the same nominal azimuth)and the same or similar offsets (or have the same nominal offset). Atstep 240, a subgroup, such as a first subgroup, of target traces isselected. The first subgroup of target traces is at the first nominaloffset and the first nominal azimuth.

At step 250, a set of input traces is selected. The input traces are thetraces from which the predicted multiples are computed. In oneembodiment, a set of input traces at half of the nominal offset for theselected subgroup of target traces is selected. However, a set of inputtraces at any offset range may be selected. In another embodiment, theinput traces are interpolated and regularized. Alternatively, severaloffsets may be merged and/or sub-stacked to improve sampling and signalto noise ratio.

At step 260, the selected input traces are preconditioned to simulatezero offset traces. The selected input traces may be preconditionedusing many techniques well known in the art, such as a moveoutcorrection, a full migration/demigration and the like. The input tracesmay be located by their midpoints. In this manner, each preconditionedinput trace simulates a trace that would have been recorded with asource and a receiver at the midpoint location.

At step 270, each pair of selected preconditioned input traces isconvolved to generate a set of convolutions. The pairs are selected suchthat the separation between the midpoints of the traces in each pair isequal to half the nominal offset for the selected subgroup of targettraces, and the azimuth of the line connecting the midpoints of thetraces in each pair is equal to the nominal azimuth of the selectedsubgroup of target traces. Each convolution may be located at themidpoint of the line connecting the midpoints of the two traces in eachpair. The input traces may also be interpolated to form theconvolutions. Actual or possible structural dips may also be taken intoaccount in forming the convolutions.

At step 280, a three dimensional operator is applied to the set ofconvolutions and the result is located at the midpoints of the selectedsubgroup of target traces. In one embodiment, the three dimensionaloperator is a three dimensional demigration operator with an offsetequal to half of the nominal offset of the selected subgroup of targettraces and a velocity equal to half the water velocity. In such a case,the result may be referred to as demigrated convolutions. Anotherexample of the three dimensional operator is a poststack (zero-offset)demigration followed by an inverse Dip Moveout (DMO) and inverse moveoutcorrection. In this manner, the surface multiples for the selectedsubgroup of target traces may be predicted. In one embodiment, theresult may be corrected from the nominal offset and azimuth to theactual offset and azimuth. For instance, the offset correction may beperformed using a differential moveout correction, and the azimuthcorrection may be performed via interpolation from adjacent azimuths.

Alternatively, the input traces may be convolved at their originaloffset and the convolutions may be corrected prior to applying the threedimensional operator. The correction may be performed using a moveoutcorrection with half the velocity (or twice the offset).

At step 285, a determination is made as to whether another subgroup,such as a second subgroup, of target traces with a second nominalazimuth exists. If the answer is in the affirmative, then processingreturns to step 240, at which the second subgroup of target traces isselected. Processing then continues through steps 250-280 at which a setof input traces (which may or may not be the same as the first set) isprocessed using the nominal azimuth of the second subgroup of targettraces in order to predict multiples for those traces. Steps 240-285continue until all the subgroups within the selected group have beenprocessed.

If the answer is in the negative, then processing continues to step 290,at which a determination is made as to whether another group, such as asecond group, of target traces with a second nominal offset exists. Ifthe answer is in the affirmative, then processing returns to step 220,at which the second group of target traces with the second nominaloffset is selected. Processing then continues through steps 230-285 atwhich one or more sets of input traces are processed using the nominaloffset for the second group of target traces in order to predictmultiples for those traces. Steps 220-290 continue until all the groupswithin the set of target traces have been processed.

In one embodiment, method 200 may be used to predict the threedimensional surface multiples when substantial streamer (cable)feathering exists. In another embodiment, method 200 may be used topredict multiples at a specified azimuth and may therefore be made toaccount for azimuth variations in the seismic data. These azimuthvariations can be a significant source of error in predicted multiples.As such, in accordance with another embodiment of the invention, method200 may be used to predict errors in a manner similar to two dimensionaland three dimensional surface multiple prediction methods described inU.S. patent application Ser. No. 10/668,927, which is incorporatedherein by reference.

FIG. 3 illustrates a flow diagram of a method 300 for performing a threedimensional surface multiple prediction in accordance with one or moreembodiments of the invention. At step 310, a set of target traces isdivided into one or more groups of traces according to offsets. The setof target traces defines the locations at which the multiples are to bepredicted. Each group contains target traces with offsets within aspecified range. As with the target traces described with reference tostep 210, each offset range may have a nominal offset value, which istypically the central offset value in that range.

At step 320, a record of input seismic traces is preconditioned. Thatis, the record of input seismic traces is separated into inputsubsurface lines (SSLs) and each input SSL is regularized according toconventional regularization methods known by persons of ordinary skillin the art. Once regularized, the crossline offset between each sourceand receiver is zero and the inline offset between each source andreceiver is regular. Once the record of input seismic traces has beenregularized to input SSL's, the regularized traces are extrapolatedaccording to conventional extrapolation methods known by persons ofordinary skill in the art. Once extrapolated, the gap between eachsource and the receiver nearest to the source on each input SSL isfilled with extrapolated receivers. As a result, each input SSL hastraces with zero crossline offset and regularly increasing inlineoffsets starting from zero.

At step 330, the two dimensional surface multiples for each input SSLare predicted using a conventional two dimensional prediction algorithmknown by persons of ordinary skill in the art. At step 340, thepredicted two dimensional surface multiples are sorted into one or moreplanes (or groups) according to offsets. Each plane contains multipleswithin a specified range of offsets. Each offset range may have anominal offset value, which is typically the central offset value inthat range. At step 345, a plane of the predicted two dimensionalsurface multiples with a nominal offset, such as a first nominal offset,is selected.

At step 350, a two dimensional operator is applied to the plane ofpredicted two dimensional surface multiples and the result is located atthe midpoints of a group of target traces with the first nominal offset.The two dimensional operator may be a two dimensional demigrationoperator with a velocity equal to half the water velocity or half theprimary velocity. In this manner, the two dimensional multipleprediction takes into account variations in the input SSL's in the inline direction and the two dimensional operator takes into accountvariations in the input SSL's in the cross line direction.

At step 360, a determination is made as to whether a plane of predictedtwo dimensional surface multiples at a second nominal offset exists. Ifthe answer is in the affirmative, then processing returns to step 345,at which a plane of predicted two dimensional surface multiples at asecond nominal offset is selected. Processing then continues to step350, at which a two dimensional operator is applied to the plane ofpredicted two dimensional surface multiples at the second nominal offsetand the result is located at the midpoints of a group of target tracesat the second nominal offset. Steps 345-360 continue until all of thepredicted two dimensional surface multiples at various nominal offsetshave been processed. In one embodiment, method 300 may be used topredict the three dimensional surface multiples when the streamers (orcables) are all substantially parallel with each other with negligiblefeathering.

The following paragraphs provide mathematical derivations for methods200 and 300 in accordance with one or more embodiments of the invention.FIG. 4 illustrates a plan view of an acquisition geometry in accordancewith one or more embodiments of the invention. Surface multiples for atrace (S, R), with source at S and receiver at R, are to be predicted. Mand h are defined as the midpoint and offset of (S, R) respectively. Xis defined as a potential downward reflection point (DRP) for thesurface multiples.

A three dimensional surface multiple prediction may be performed byconvolving trace (S, X) with trace (X, R), and summing theseconvolutions over all possible X. In order to do this, traces (S, X) and(X, R) generally need to be estimated from recorded traces. One way toestimate trace (S, X) is to apply a differential moveout correction to atrace with a similar midpoint, M_(S), from the recorded dataset and witha similar offset. Trace (X, R) may be estimated in like manner. Thedifferential moveout applied to the trace used to estimate (S, X)depends not only on its offset, but also on the offset from S to X,denoted x_(s) in FIG. 4. Typically, a given recorded trace will only beused to estimate one trace for each subsurface line (SSL) in theprediction process, but it will be used on every SSL within a givenaperture of the recorded trace. As such, the differential moveoutcorrection and convolution may be repeated for every SSL for which theprediction is required.

The order of the moveout correction and the convolution may be swapped,i.e. traces with midpoints at M_(S) and M_(R) are convolved before themoveout correction is applied, and the resultant error may be correctedafter the convolution. The recorded traces may be processed toapproximate (kinematically) zero-offset traces at the midpointlocations. For a simple, first-order multiple from a horizontalreflector at time to in a constant velocity medium with velocity v, theprimary reflections occur at time to on the zero-offset traces at M_(S)and M_(R), and at time 2t₀ on their convolution. The travel time for theprimary reflection on trace (S, X) is given by:

t(S, X)=[t ₀ ²+(x _(S) /v)²]^(1/2)  (1)

and similarly for the trace (X, R), such that the travel time on theconvolution of these traces is given by

t(X)=[t ₀ ²+(x _(S) /v)²]^(1/2) +[t ₀ ²+(x _(R) /v)²]^(1/2)  (2)

Accordingly, a post-convolution correction is performed to map an eventat time 2t₀ time t(X). At this point, a constant velocity demigrationoperator with velocity V maps a migrated time t_(m) to a demigrated timet_(d) given by:

t _(d)=[(t _(m)/2)²+(X _(S) /V)²]^(1/2)+[(t _(m)/2)²+(X _(R)/V)²]^(1/2)  (3)

where X_(S) and X_(R) are the distances from the migrated location tothe demigrated source and receiver locations, respectively. Equation (3)has a similar form to the post-convolution correction. Thus, ift_(m)=2t₀ and t_(d)=t(X), then equation (3) becomes

t(X)=[t ₀ ²+(X _(S) /V)²]^(1/2) +[t ₀ ²+(X _(R) /V)²]^(1/2)  (4)

Equation (4) would be identical to the post-convolution correction ifthe convolved trace is located at X, and X_(S)=x_(S), X_(R)=x_(R) andV=v. However, the location X depends on S and R, and hence thedemigration would be repeated whenever a change in (S, R) creates achange in X. The convolved trace may be located at the “migratedlocation” M_(X), the midpoint of M_(S) and M_(R), which does not dependon S or R, but only on M_(S) and M_(R). If S′ and R′ are defined to bethe midpoints of (S, M) and (M, R) respectively, then the distances fromM_(X) to S′ and R′ are x_(S)/2 and x_(R)/2 respectively. Hence,demigration from M_(X) to (S′,R′) yields a demigrated traveltime givenby equation (3) with X_(S)=x_(S)/2 and X_(R)=x_(R)/2, which provides thedesired demigrated traveltime if velocity V=v/2.

Notably, (M_(S), M_(R)) is parallel to (S, R) and the offset from M_(S)to M_(R) is h/2. Accordingly, if the offset and azimuth of (S, R) arefixed, then the offset and azimuth of (M_(S), M_(R)) are also known. IfM_(X) is determined and a volume of data that has been kinematicallymapped to zero offset is available, then M_(S) and M_(R) may bedetermined, and the corresponding traces may be convolved from thisvolume, thereby placing the convolution into a new volume at locationM_(X). Repeating this operation for all M_(X), and then demigrating thevolume at constant offset (h/2) and azimuth (as defined previously) withvelocity v/2 yields the predicted multiples for the entire offset plane.

One distinction between the embodiments of the invention and the priorart is that the traces are convolved at fixed (often zero) offset beforethe offset is corrected. The improved performance accomplished byvarious embodiments of the invention is attained by making someapproximations in the derivation of the algorithm. Theory and tests haveshown that, in many cases, the predicted multiples are stillsufficiently accurate that they may be adaptively subtracted. Whencrossline dip effects are significant, multiples predicted using variousembodiments of the invention will be much more accurate than thosepredicted by a two dimensional surface multiple algorithm.

FIG. 5 illustrates a computer network 500, into which embodiments of theinvention may be implemented. The computer network 500 includes a systemcomputer 530, which may be implemented as any conventional personalcomputer or workstation, such as a UNIX-based workstation. The systemcomputer 530 is in communication with disk storage devices 529, 531, and533, which may be external hard disk storage devices. It is contemplatedthat disk storage devices 529, 531, and 533 are conventional hard diskdrives, and as such, will be implemented by way of a local area networkor by remote access. Of course, while disk storage devices 529, 531, and533 are illustrated as separate devices, a single disk storage devicemay be used to store any and all of the program instructions,measurement data, and results as desired.

In one embodiment, seismic data from hydrophones are stored in diskstorage device 531. The system computer 530 may retrieve the appropriatedata from the disk storage device 531 to perform the 3-D surfacemultiple prediction according to program instructions that correspond tothe methods described herein. The program instructions may be written ina computer programming language, such as C++, Java and the like. Theprogram instructions may be stored in a computer-readable memory, suchas program disk storage device 533. Of course, the memory medium storingthe program instructions may be of any conventional type used for thestorage of computer programs, including hard disk drives, floppy disks,CD-ROMs and other optical media, magnetic tape, and the like.

According to the preferred embodiment of the invention, the systemcomputer 530 presents output primarily onto graphics display 527, oralternatively via printer 528. The system computer 530 may store theresults of the methods described above on disk storage 529, for lateruse and further analysis. The keyboard 526 and the pointing device(e.g., a mouse, trackball, or the like) 525 may be provided with thesystem computer 530 to enable interactive operation.

The system computer 530 may be located at a data center remote from thesurvey region. The system computer 530 is in communication withhydrophones (either directly or via a recording unit, not shown), toreceive signals indicative of the reflected seismic energy. Thesesignals, after conventional formatting and other initial processing, arestored by the system computer 530 as digital data in the disk storage531 for subsequent retrieval and processing in the manner describedabove. While FIG. 5 illustrates the disk storage 531 as directlyconnected to the system computer 530, it is also contemplated that thedisk storage device 531 may be accessible through a local area networkor by remote access. Furthermore, while disk storage devices 529, 531are illustrated as separate devices for storing input seismic data andanalysis results, the disk storage devices 529, 531 may be implementedwithin a single disk drive (either together with or separately fromprogram disk storage device 533), or in any other conventional manner aswill be fully understood by one of skill in the art having reference tothis specification.

While the foregoing is directed to embodiments of the present invention,other and further embodiments of the invention may be devised withoutdeparting from the basic scope thereof, and the scope thereof isdetermined by the claims that follow.

1-21. (canceled)
 22. A method for predicting a plurality of surfacemultiples for a plurality of traces in a record of seismic data,comprising: providing a plurality of target traces at a nominal offset;predicting a plurality of two dimensional surface multiples for aplurality of input subsurface lines; and applying a two dimensionaloperator to the predicted two dimensional surface multiples.
 23. Themethod of claim 22, further comprising, prior to predicting the twodimensional surface multiples, preconditioning the input traces into theinput subsurface lines.
 24. The method of claim 22, further comprisingsorting the predicted two dimensional surface multiples into one or moreplanes according to offsets, wherein each plane has an associatednominal offset.
 25. The method of claim 24, further comprising selectinga plane of predicted two dimensional surface multiples having a firstnominal offset.
 26. The method of claim 22, wherein applying the twodimensional operator comprises locating the result of the twodimensional operator application at the midpoints of the target traces.27. The method of claim 22, wherein the two dimensional operator is atwo dimensional demigration operator with a velocity equal to one ofhalf the water velocity or half of a multiple velocity function.
 28. Amethod for predicting a plurality of surface multiples for a pluralityof traces in a record of seismic data, comprising: predicting surfacemultiples for subsurface lines in the record using a two dimensionalconvolutional algorithm; selecting a plurality of target traces; andapplying a velocity dependent operator to tailor and combine thepredicted surface multiples for each target trace.
 29. The method ofclaim 28, wherein applying the velocity dependent operator to tailor andcombine the predicted surface multiples comprises locating the predictedsurface multiples at the midpoints of the target traces.
 30. The methodof claim 28, wherein the predicted surface multiples subject to thevelocity dependent operator are selected for each target trace based onthe offset of the target trace.
 31. The method of claim 28, wherein thepredicted surface multiples subject to the velocity dependent operatorare selected for each target trace based on the azimuth of the targettrace.
 32. The method of claim 28, wherein the velocity dependentoperator is a two or higher dimensional operator.
 33. The method ofclaim 28, wherein applying the velocity dependent operator comprisescrossline stacking the predicted surface multiples.
 34. The method ofclaim 28, wherein the velocity dependent operator comprises at least oneof a dip moveout, an inverse dip moveout, a normal moveout, an inversenormal moveout, a migration and a demigration.
 35. The method of claim28, wherein the velocity dependent operator comprises an inverse normalmoveout and stack.
 36. The method of claim 28, wherein the subsurfacelines are obtained by regularizing the recorded seismic traces.
 37. Themethod of claim 28, wherein the target traces provide the locations atwhich the surface multiples are predicted.
 38. The method of claim 28,wherein the velocity dependent operator employs a velocity equal to oneof half the water velocity or half of a multiple velocity function. 39.The method of claim 28, further comprising: sorting the predictedsurface multiples into crossline order; and interpolating the predictedsurface multiples in the crossline direction prior to application of thevelocity dependent operator.
 40. The method of claim 28, furthercomprising dividing the target traces into nominal offset ranges priorto application of the velocity dependent operator.
 41. The method ofclaim 28, further comprising dividing the target traces into nominalazimuth ranges prior to application of the velocity dependent operator.